Systems and methods for independent audit and assessment framework for ai systems

Patent metadata

Field	Value
Publication number	US-2025148026-A1
Application number	US-202418942117-A
Country	US
Kind code	A1
Filing date	Nov 8, 2024
Priority date	Nov 8, 2023
Publication date	May 8, 2025
Grant date	—

Abstract

Example systems and methods for AI capability assessment include query-based assessment of sequential decision making agents (SDMAs) in stochastic settings with minimal assumptions on SDMA internals. In these examples, a new approach is presented for modeling the capabilities of black-box AI systems by using an active learning approach that can effectively interact with the black-box AI systems and learn an interpretable probabilistic model describing the capabilities of the black-box AI systems

First claim

1 . A method for autonomous assessment of sequential decision-making systems in stochastic settings, comprising:
accessing an input A, that is the software and/or integrated hardware constituting an agent capable of sequential decision making, or planning, and a simulator Sim that the agent A can interact with to execute its behavior; inducing a system that accepts users' specification of objectives and computes and executes the behavior necessary for achieving them. learning a model defining capabilities of the SDM system using query-based autonomous capability estimation, including:
defining M as the space of capability models expressible in terms of the set of interpretable concepts or predicates known to the user,
discovering and learning the true capabilities of the agent A as a model in M in a manner that clarifies to the user the A's capabilities and the effects of executing them, by posing queries to the agent of the SDM system and utilizing responses to the queries as data to eliminate inconsistent models from the set of capability models M, wherein the model as learned models the set of A's capabilities in stochastic settings.

2 . The method of claim 1 , further comprising measuring a variational distance between the model and a true transition model associated with the SDMA system to evaluate how close the model is to the true transition model.

3 . The method of claim 1 , further comprising using object-centric predicate representation including an abstraction of environment states to high-level logical states expressible in predicate vocabulary to induce the set of transition models.

4 . The method of claim 1 , wherein the model as learned is a probabilistic model expressed in terms of the set of predicates and the set of capabilities.

5 . The method of claim 1 , wherein the model as learned is a non-deterministic model expressed in terms of the set of predicates and the set of capabilities.

6 . The method of claim 1 , wherein the set of predicates are first order logic predicates and the evaluation functions include Boolean evaluation functions.

7 . The method of claim 1 , wherein the model is learned using input concept vocabulary modeled as binary-valued predicates.

8 . The method of claim 1 , wherein the model is learned by utilizing syntax and semantics of known SDM model representation language and probabilistic planning definition language.

Assignees

• Srivastava Siddharth
• Verma Pulkit
• Univ Arizona State

Inventors

• Srivastava Siddharth
• Verma Pulkit

Classifications

• G06F16/29

  Geographical information databases · CPC title

• G06F16/243

  Natural language query formulation · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06F16/953 Primary

  Querying, e.g. by the use of web search engines · CPC title